Elsevier

Energy

Volume 195, 15 March 2020, 116953
Energy

Comparative analysis of energy costs on farms in the European Union: A nonparametric approach

https://doi.org/10.1016/j.energy.2020.116953Get rights and content

Highlights

  • It was made an efficiency analysis of energy costs in EU farms.

  • Data at farm and regional level from the FADN were considered.

  • It was performed a DEA approach after factor and cluster analysis.

  • There are possibilities of reducing the costs of energy in many EU regions.

Abstract

A rational and efficient use of the various sources of energy available to farms allows, not only for cost cutting, but also, a reduction in the environmental impacts bringing positive externalities and contributions toward sustainability. Of course, this efficiency depends on several factors that may influence the dynamics and performance of the farms in question which may also change spatially between countries and regions. These questions related to the great diversity of realities in the agricultural sector are especially relevant within the European Union (EU) context. In this framework, the main objective of the research presented here is to make an efficiency analysis of energy costs in farms across EU countries and regions, stressing possibilities of savings and taking into account the several realities. For this purpose, data at farm level from the Farm Accountancy Data Network (FADN) was considered for the period 2014–2016 which were then explored through the nonparametric approach Data Envelopment Analysis (DEA). For the nonparametric analysis the Cobb-Douglas model was considered as a base. In this way, the total production (euros) was considered as output. Paid labour (hours), the total fixed assets (euros) and the energy costs (euros) were considered as inputs. In an alternative attempt to take into account the different realities across the EU countries the statistical information in monetary units was corrected by the Price Level Indices and was deflated by the Harmonised Indices of Consumer Prices. Furthermore, to alternatively consider the diversity of contexts of the farms from the EU, the several regions and countries here were clustered through cluster analysis, after factor analysis so as to avoid problems of collinearity. As main insights, it is worthy of stressing the possibilities of significantly reducing the costs of energy consumption in farms from many EU regions. For example, it is possible to reduce the costs of energy use by about 55% in Pohjanmaa (Finland), 53% in Cyprus, 56% in Makedonia-Thraki (Greece), 52% in Thessalia (Greece), 56% in Puglia (Italy) and 53% in Basilicata (Italy). In these contexts the Common Agricultural Policy (CAP) should play a determinant role.

Introduction

The questions related with the sustainability of energy use are a great concern for the several stakeholders around the world, as well as, across the different social and economic sectors. These aspects take special relevance in the agricultural sector, considering its particularities. The efficiency analysis may provide an interesting contribution here and within the efficiency approaches, the DEA holds particular importance, considering the kind of outcomes that it can afford [1].

The Data Envelopment Analysis is a useful nonparametric approach considered usually to analyse the efficiency and the productivity between Decision Making Units (DMUs). However, this methodology benchmarks the several DMUs and presupposes homogeneity between them. When these assumptions may not be accepted, it is crucial to create a cluster of homogeneous DMUs [2].

The diversity of realities and the lack of homogeneity is particularly significant in the European Union agricultural sector. Many aspects contribute towards this diversity, such as natural factors (soil, climate, and natural landscape), social conditions, political and legal frameworks and economic indicators. It is not easy to consider all these factors and often it is almost impossible to obtain information in order to quantify them [3].

However, as motivation background and theoretical analysis/modelling, the literature review (complemented with bibliometric analysis) presented below shows that on the subject of efficient energy consumption in the agricultural sector within the EU, there are few scientific studies, less so at a regional level and almost none with microeconomics data at farm level from all regions and countries. It would appear that there seems to be a knowledge gap here that needs to be further explored. The data at farm level allows capturing for other kinds of dynamics (namely farm scale economies having implications on the numerous farm variables) which justifies being addressed specifically [1].

In this context, it seems pertinent to identify the most efficient EU countries and regions, in terms of farming energy consumption, through efficiency approaches and microeconomics data. This will be important for further planning of the European agricultural sector, specifically for agricultural policy design. In fact, the most efficient European countries and regions may be considered as benchmarks for the design of new policy instruments in the context of the Common Agricultural Policy. On the other hand, it is important to create specific strategies to improve the performance of those less efficient regions and countries, in a framework of better sustainability. For this purpose statistical information was considered at farm level from the European Union Farm Accountancy Data Network [4] in average for the period 2014–2016. An average was considered for this period to, in an alternative way, take into account the eventual annual changes in the farms’ variables derived from natural or agronomic changes and cycles. This information was explored through DEA methodologies with the DEAP [5] software. In this analysis it a model based on the Cobb and Douglas [6] theory of production was utilized, where the output is the total production (euros) and the inputs are the total labour paid (hours), the total fixed assets (euros, proxy for the capital) and the energy costs (euros). In an alternative approach so as to take into account the heterogeneity between the farms of the EU regions the variables in euros were corrected and deflated, respectively, with the Price Level Indices and the Harmonised Indices of Consumer Prices obtained from the Eurostat [7]. To correct the total agricultural output the Price Level Indices from the gross domestic product were considered, for the total fixed assets those from gross fixed capital formation were considered and for the energy costs from electricity, gas and other fuels. For the Harmonised Indices of Consumer Prices the average annual index for all items was considered. After these adjustments the several regions and countries were clustered, after factor analysis (to avoid problems of collinearity), following Stata [8] and Torres-Reyna [9] procedures.

The novelty/originality of the work presented here is related with the consideration of an alternative approach/methodology, to analyse the efficient energy consumption by the EU agricultural sector, which combines data at a regional and farm level (with the monetary statistical information corrected, to spatial effects, and deflated to remove the difference of prices across years and countries/regions) explored through DEA, after factor and cluster (FC) analysis. In other words, a DEA-FC methodology was used, based on a Cobb-Douglas model and statistical information at farm level (corrected to consider the specific characteristics of each country). It is important to highlight that the great novelty here is the consideration of alternative approaches in order to deal with the particularities of the farming sector, as well as with the heterogeneity of the farms across the EU regions and countries (taken into account through the spatial correction and deflation of the monetary variables and through the factor plus cluster analysis). These alternatives also have limitations, as others often do, however the results presented here are one more contribution towards understanding the agricultural sector within the EU framework.

After this introduction this study will be organized into five subsequent sections. The second section will be for the literature review with documents obtained from all the Web of Science databases [10] and Scopus [11]. This literature survey was complemented with bibliometric analysis with the VOSviewer software [12]. The third section for data analysis, the fourth for factor and cluster approaches, the fifth for the efficiency analysis and the sixth for the main findings.

Section snippets

Bibliometric analysis and literature review

Fig. 1 presents a network visualization map of terms co-occurrences (considering 4 as the minimum number of occurrence of a term) obtained through the VOSviewer software with scientific documents (about forty articles) obtained from the Web of Science (all databases) and Scopus scientific platforms for the following topics: Energy consumption; Agricultural sector; European Union; Efficiency. The terms with greater circles are those with more occurrences.

In Fig. 1 it is possible to identify

Data analysis

In Fig. 2 when focussing on the evolution of the main variables considered in this study it shows that Slovakia, Czech Republic and the Netherlands are the three EU countries having a greater total output, paid labour and energy costs. On the other hand, the Netherlands, the United Kingdom and Denmark are those having more total fixed assets.

Amongst the countries which possess lower values, at farm level, for these variables we can see, for example, Poland, Portugal, Cyprus, Croatia, Slovenia,

Factor and cluster analysis

Considering the heterogeneity between the EU regions and countries an additional alternative approach was to create a cluster with the variables considered in this study [2]. However, to avoid problems of collinearity the variables were first analysed through factor analysis. For the factor and cluster analysis the Stata [8] and Torres-Reyna [9] procedures were considered.

The results in Table 1 were obtained through factor analysis, considering the several European Union regions and countries

Efficiency study through DEA

In this section, following, for example, Martinho [1], the efficiency for EU countries and regions will be analysed through Data Envelopment Analysis (input oriented and multi-stage) and with the DEAP software. As a base the Cobb-Douglas model was considered, where the output is the total farming production and the inputs are the paid labour, total fixed assets and energy costs. The statistical information, at farm level, was obtained from the Farm Accountancy Data Network. All variables in

Discussions and main findings

The study presented here aimed to analyse, namely, the energy costs incurred by farms from the European Union regions and countries. Statistical information was considered, at farm level, from the Farm Accountancy Data Network, in average, for the period 2014–2016. This is one the main contributions for the several stakeholders, including the scientific community, because, in fact, it is not so usual the consideration of data at farm level from the FADN [1,37]. To take the heterogeneity between

Conclusions

In this conclusions section, the main contributions of the manuscript will be listed point wise, as following:

  • 1.

    It is important to promote the sustainable energy use in the agricultural sector (upstream and downstream), namely considering renewable sources of energy (some from inside the sector) and improving the efficiency to mitigate the environmental impacts. In these contexts, the agricultural realities from some EU countries, as Spain, France and Italy, for example, should be taken into

Acknowledgments

This work is financed by national funds through FCT - Fundação para a Ciência e Tecnologia, I.P., under the project UID/Multi/04016/2019. Furthermore we would like to thank the Instituto Politécnico de Viseu and CI&DETS for their support. This work is supported by national funds, through the FCT – Portuguese Foundation for Science and Technology under the project UID/SOC/04011/2019.

References (45)

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